Synthetic to Real

Synthetic-to-real (Syn-to-Real) research focuses on bridging the performance gap between models trained on readily available synthetic data and their application to real-world scenarios. Current efforts concentrate on improving domain adaptation techniques, often employing generative adversarial networks (GANs), knowledge distillation, and various unsupervised domain adaptation (UDA) methods, including those leveraging transformers and other deep learning architectures. Success in this area is crucial for advancing numerous fields, from medical image analysis and autonomous driving to speech recognition and drug discovery, where generating large, high-quality real-world datasets is often impractical or impossible. The ultimate goal is to reliably leverage the benefits of synthetic data while mitigating the limitations imposed by the inherent differences between synthetic and real-world data distributions.